2027 Illinois MCS-DS vs. Grand Canyon Online MS in Data Science: Cost, Curriculum, Admissions, and Format

The distinction is the degree each university awards and the course plan behind it. This overview identifies what to compare before deciding whether either program fits your goals.

The program that looks better on paper may be the poorer choice if its prerequisites, course availability, or weekly schedule do not work for you. Request a current degree map from each school and compare the courses you would actually take.

Neither program can be named the cheaper choice without comparable, current quotes. Tuition schedules can change, and a per-credit rate does not show the full cost of a degree. Ask each university for an estimate tied to your intended start term and expected course load.

Check the same cost components for both programs: tuition for every required credit, mandatory university and course fees, and any required materials or technology. Then account separately for financing charges, if you would borrow, and the income or work hours you might give up to study. The online master's in data science cost comparisons can help you judge whether either quote is competitive, but a published comparison is not a substitute for your own estimate.

Ask how repeated courses, changes in tuition, scholarships, and employer reimbursement would affect your net cost. A common mistake is to treat an advertised tuition figure as the amount you will ultimately pay.

Illinois's MCS-DS approaches data science through a computer science degree. Grand Canyon's MS names data science as the degree field. That distinction helps frame the comparison, but it does not establish that every Illinois course is more technical or that every Grand Canyon course is more applied.

Read the current course descriptions side by side. Pay particular attention to programming, algorithms, statistics, machine learning, data management, and opportunities to complete substantial projects. If responsible AI is a career priority, check whether it appears in assessed coursework rather than only in a program description; artificial intelligence graduate programs in ethics and governance offer a useful point of comparison.

Choose Illinois's curriculum if its available computing and data science courses match the technical skills you want to build. Consider Grand Canyon if its required sequence addresses your gaps more directly. In either case, confirm which electives are available to online students and whether a project you want to complete is required, optional, or unsupported.

Both programs require applicants to meet their universities' graduate admissions rules, but their current program-specific requirements should be checked directly before you apply. In particular, do not assume that a bachelor's degree outside computer science either disqualifies you from Illinois or automatically qualifies you for Grand Canyon.

For Illinois, examine the stated expectations in programming, mathematics, and computer science, then identify how your transcript or professional work demonstrates them. For Grand Canyon, confirm the current GPA policy, required documents, and any preparation expected for its data science courses. Applicants changing fields can use AI master's degrees for students from other academic fields to think through preparation gaps, while remembering that preparation advice is not an admissions waiver.

Before paying an application fee, send each admissions office your academic background and ask which requirements you meet, which evidence you should submit, and whether any deficiencies can be addressed before enrollment.

Illinois's MCS-DS requires 32 credits. Credit requirements alone, however, do not determine how quickly you can finish: course availability, prerequisites, and the number of courses you can manage alongside work all affect the timeline.

Ask both schools for a term-by-term plan based on your intended pace, including the earliest realistic graduation term and any maximum time-to-degree rule. If speed is your main priority, review accelerated data science master's pathways as alternatives. Do not select a faster-looking schedule until you know whether its weekly workload is sustainable.

Illinois delivers its online MCS coursework through a Coursera-based format, while Grand Canyon uses its own online learning platform. The practical comparison is not the platform name but how instruction, deadlines, assessments, instructor access, and group work fit your week.

Before enrolling, request a sample course schedule or syllabus from each program. Check whether any meetings or exams must occur at set times, whether courses can be taken during busy work periods, and how students get help when they are stuck. An online label does not mean every activity can be completed whenever you choose.

Both the University of Illinois Urbana-Champaign and Grand Canyon University hold institutional accreditation from the Higher Learning Commission. That is the relevant institutional check for these degrees; do not assume either program has a separate data science-specific accreditation.

Accreditation establishes an institutional standard, not that two curricula are equivalent or that an employer will prefer one credential. If you plan to use employer tuition assistance, transfer credit, or pursue further study, confirm the receiving organization's own eligibility rules before committing.

Either program may support applications for data-focused roles, but your prior experience and demonstrated skills will shape which roles are realistic. Illinois's computer science framing may appeal to someone targeting work that draws heavily on software and computing; Grand Canyon's data science degree may appeal to someone seeking a curriculum explicitly organized around that field. Neither degree title proves proficiency in a particular tool or guarantees a job.

Compare each course plan against actual vacancies you would pursue, then look for opportunities to produce work you can explain to an employer: a documented analysis, a tested model, or a reproducible data pipeline. If professional connections matter to your search, compare the schools' actual career services and graduate communities with master's-level AI programs with alumni networking opportunities; do not infer networking access from a university's name alone.

The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. That is an occupation-wide figure, not a starting salary or an outcome reported for graduates of either university. Your pay may differ substantially with experience, location, employer, and role.

BLS projects 35% employment growth for data scientists from 2025 to 2035. The outlook supports investigating the field, but growth does not remove competition for individual jobs. Evaluate a program by the skills and work samples it can help you develop, not by applying the national median wage to a personal return-on-investment calculation.

Choose Illinois if you want a Master of Computer Science and its current course plan fits your technical goals and preparation. Choose Grand Canyon if its MS in Data Science sequence better addresses your learning needs and its schedule and verified total cost work better for you. Neither is a universal winner.

Make the final comparison using the same evidence for each school. This short check is designed to catch the most consequential mismatches before you enroll:

1. Obtain current degree maps and mark the required courses and electives you would actually take.
2. Confirm admissions eligibility and any preparation you must complete before starting.
3. Request comparable written cost estimates and realistic term-by-term completion plans.
4. Review sample syllabi for weekly deadlines, assessments, projects, and instructor access.
5. Match the skills you would build against job postings relevant to your experience and target role.

If one school cannot clarify a material requirement or cost, treat that uncertainty as part of the decision rather than filling the gap with an assumption.

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• Academic Years, Academic Calendars, Payment Periods, and Disbursements | 2025-2026 Federal Student Aid Handbook https://fsapartners.ed.gov/knowledge-center/fsa-handbook/2025-2026/vol3/ch1-academic-years-academic-calendars-payment-periods-and-disbursements
• Chapter 3 - Courses and Enrollment, Full Course of Study, and Reduced Course Load | USCIS https://www.uscis.gov/policy-manual/volume-2-part-f-chapter-3
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• What's the Difference Between Asynchronous and Synchronous Learning? | Ohio State Online https://online.osu.edu/content-hub/blogs/whats-the-difference-between-asynchronous-and-synchronous-learning/